CS2351 ARTIFICIAL INTELLIGENCE SYLLABUS | ANNA UNIVERSITY BTECH IT 7TH SEMESTER SYLLABUS REGULATION 2008 2011-2012 BELOW IS THE ANNA UNIVERSITY SEVENTH SEMESTER B.TECH. INFORMATION TECHNOLOGY DEPARTMENT SYLLABUS IT IS APPLICABLE FOR ALL STUDENTS ADMITTED IN THE YEAR 2011-2012 (ANNA UNIVERSITY CHENNAI,TRICHY,MADURAI,TIRUNELVELI,COIMBATORE), 2008 REGULATION OF ANNA UNIVERSITY CHENNAI AND STUDENTS ADMITTED IN ANNA UNIVERSITY CHENNAI DURING 2009
CS2351 ARTIFICIAL INTELLIGENCE L T P C
3 0 0 3
AIM:
To learn the basics of designing intelligent agents that can solve general purpose
problems, represent and process knowledge, plan and act, reason under uncertainty and
can learn from experiences
UNIT I PROBLEM SOLVING 9
Introduction – Agents – Problem formulation – uninformed search strategies – heuristics
– informed search strategies – constraint satisfaction
UNIT II LOGICAL REASONING 9
Logical agents – propositional logic – inferences – first-order logic – inferences in firstorder
logic – forward chaining – backward chaining – unification – resolution
UNIT III PLANNING 9
Planning with state-space search – partial-order planning – planning graphs – planning
and acting in the real world
UNIT IV UNCERTAIN KNOWLEDGE AND REASONING 9
Uncertainty – review of probability - probabilistic Reasoning – Bayesian networks –
inferences in Bayesian networks – Temporal models – Hidden Markov models
UNIT V LEARNING 9
Learning from observation - Inductive learning – Decision trees – Explanation based
learning – Statistical Learning methods - Reinforcement Learning
TOTAL: 45PERIODS
73
TEXT BOOK
1. S. Russel and P. Norvig, “Artificial Intelligence – A Modern Approach”, Second
Edition, Pearson Education, 200UNIT III
REFERENCES
1. David Poole, Alan Mackworth, Randy Goebel, ”Computational Intelligence : a
logical approach”, Oxford University Press, 2004.
2. G. Luger, “Artificial Intelligence: Structures and Strategies for complex problem
solving”, Fourth Edition, Pearson Education, 2002.
3. J. Nilsson, “Artificial Intelligence: A new Synthesis”, Elsevier Publishers, 1998.
CS2351 ARTIFICIAL INTELLIGENCE L T P C
3 0 0 3
AIM:
To learn the basics of designing intelligent agents that can solve general purpose
problems, represent and process knowledge, plan and act, reason under uncertainty and
can learn from experiences
UNIT I PROBLEM SOLVING 9
Introduction – Agents – Problem formulation – uninformed search strategies – heuristics
– informed search strategies – constraint satisfaction
UNIT II LOGICAL REASONING 9
Logical agents – propositional logic – inferences – first-order logic – inferences in firstorder
logic – forward chaining – backward chaining – unification – resolution
UNIT III PLANNING 9
Planning with state-space search – partial-order planning – planning graphs – planning
and acting in the real world
UNIT IV UNCERTAIN KNOWLEDGE AND REASONING 9
Uncertainty – review of probability - probabilistic Reasoning – Bayesian networks –
inferences in Bayesian networks – Temporal models – Hidden Markov models
UNIT V LEARNING 9
Learning from observation - Inductive learning – Decision trees – Explanation based
learning – Statistical Learning methods - Reinforcement Learning
TOTAL: 45PERIODS
73
TEXT BOOK
1. S. Russel and P. Norvig, “Artificial Intelligence – A Modern Approach”, Second
Edition, Pearson Education, 200UNIT III
REFERENCES
1. David Poole, Alan Mackworth, Randy Goebel, ”Computational Intelligence : a
logical approach”, Oxford University Press, 2004.
2. G. Luger, “Artificial Intelligence: Structures and Strategies for complex problem
solving”, Fourth Edition, Pearson Education, 2002.
3. J. Nilsson, “Artificial Intelligence: A new Synthesis”, Elsevier Publishers, 1998.
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